def get_spectrum_vs_paramvals(self, param_vals, update_hilbertspace, evals_count=10, get_eigenstates=False,
param_name="external_parameter", num_cpus=settings.NUM_CPUS):
"""Return eigenvalues (and optionally eigenstates) of the full Hamiltonian as a function of a parameter.
Parameter values are specified as a list or array in `param_vals`. The Hamiltonian `hamiltonian_func`
must be a function of that particular parameter, and is expected to internally set subsystem parameters.
If a `filename` string is provided, then eigenvalue data is written to that file.
Parameters
----------
param_vals: ndarray of floats
array of parameter values
update_hilbertspace: function
update_hilbertspace(param_val) specifies how a change in the external parameter affects
the Hilbert space components
evals_count: int, optional
number of desired energy levels (default value = 10)
get_eigenstates: bool, optional
set to true if eigenstates should be returned as well (default value = False)
param_name: str, optional
name for the parameter that is varied in `param_vals` (default value = "external_parameter")
num_cpus: int, optional
number of cores to be used for computation (default value: settings.NUM_CPUS)
Returns
-------
SpectrumData object
"""
target_map = cpu_switch.get_map_method(num_cpus)
if get_eigenstates:
func = functools.partial(self._esys_for_paramval, update_hilbertspace=update_hilbertspace,
evals_count=evals_count)
with utils.InfoBar("Parallel computation of eigenvalues [num_cpus={}]".format(num_cpus), num_cpus):
eigensystem_mapdata = list(target_map(func, tqdm(param_vals, desc='Spectral data', leave=False,
disable=(num_cpus > 1))))
eigenvalue_table, eigenstate_table = spec_utils.recast_esys_mapdata(eigensystem_mapdata)
else:
func = functools.partial(self._evals_for_paramval, update_hilbertspace=update_hilbertspace,
evals_count=evals_count)
with utils.InfoBar("Parallel computation of eigensystems [num_cpus={}]".format(num_cpus), num_cpus):
eigenvalue_table = list(target_map(func, tqdm(param_vals, desc='Spectral data', leave=False,
disable=(num_cpus > 1))))
eigenvalue_table = np.asarray(eigenvalue_table)
eigenstate_table = None
return storage.SpectrumData(eigenvalue_table, self.get_initdata(), param_name, param_vals,
state_table=eigenstate_table)
def get_spectrum_vs_paramvals(
self,
param_name: str,
param_vals: ndarray,
evals_count: int = 6,
subtract_ground: bool = False,
get_eigenstates: bool = False,
filename: str = None,
num_cpus: int = settings.NUM_CPUS,
) -> SpectrumData:
"""Calculates eigenvalues/eigenstates for a varying system parameter,
given an array of parameter values. Returns a `SpectrumData` object with
`energy_data[n]` containing eigenvalues calculated for parameter value
`param_vals[n]`.
Parameters
----------
param_name:
name of parameter to be varied
param_vals:
parameter values to be plugged in
evals_count:
number of desired eigenvalues (sorted from smallest to largest)
(default value = 6)
subtract_ground:
if True, eigenvalues are returned relative to the ground state eigenvalue
(default value = False)
get_eigenstates:
return eigenstates along with eigenvalues (default value = False)
filename:
file name if direct output to disk is wanted
num_cpus:
number of cores to be used for computation
(default value: settings.NUM_CPUS)
"""
previous_paramval = getattr(self, param_name)
tqdm_disable = num_cpus > 1 or settings.PROGRESSBAR_DISABLED
target_map = get_map_method(num_cpus)
if not get_eigenstates:
func = functools.partial(self._evals_for_paramval,
param_name=param_name,
evals_count=evals_count)
with InfoBar(
"Parallel computation of eigensystems [num_cpus={}]".
format(num_cpus),
num_cpus,
):
eigenvalue_table = list(
target_map(
func,
tqdm(
param_vals,
desc="Spectral data",
leave=False,
disable=tqdm_disable,
),
))
eigenvalue_table = np.asarray(eigenvalue_table)
eigenstate_table = None
else:
func = functools.partial(self._esys_for_paramval,
param_name=param_name,
evals_count=evals_count)
with InfoBar(
"Parallel computation of eigenvalues [num_cpus={}]".format(
num_cpus),
num_cpus,
):
# Note that it is useful here that the outermost eigenstate object is
# a list, as for certain applications the necessary hilbert space
# dimension can vary with paramvals
eigensystem_mapdata = list(
target_map(
func,
tqdm(
param_vals,
desc="Spectral data",
leave=False,
disable=tqdm_disable,
),
))
eigenvalue_table, eigenstate_table = recast_esys_mapdata(
eigensystem_mapdata)
if subtract_ground:
for param_index, _ in enumerate(param_vals):
eigenvalue_table[param_index] -= eigenvalue_table[param_index][
0]
setattr(self, param_name, previous_paramval)
specdata = SpectrumData(
eigenvalue_table,
self.get_initdata(),
param_name,
param_vals,
state_table=eigenstate_table,
)
if filename:
specdata.filewrite(filename)
return SpectrumData(
eigenvalue_table,
self.get_initdata(),
param_name,
param_vals,
state_table=eigenstate_table,
)
def get_spectrum_vs_paramvals(self,
param_name,
param_vals,
evals_count=6,
subtract_ground=False,
get_eigenstates=False,
filename=None,
num_cpus=settings.NUM_CPUS):
"""Calculates eigenvalues/eigenstates for a varying system parameter, given an array of parameter values.
Returns a `SpectrumData` object with `energy_data[n]` containing eigenvalues calculated for
parameter value `param_vals[n]`.
Parameters
----------
param_name: str
name of parameter to be varied
param_vals: ndarray
parameter values to be plugged in
evals_count: int, optional
number of desired eigenvalues (sorted from smallest to largest) (default value = 6)
subtract_ground: bool, optional
if True, eigenvalues are returned relative to the ground state eigenvalue (default value = False)
get_eigenstates: bool, optional
return eigenstates along with eigenvalues (default value = False)
filename: str, optional
file name if direct output to disk is wanted
num_cpus: int, optional
number of cores to be used for computation (default value: settings.NUM_CPUS)
Returns
-------
SpectrumData object
"""
previous_paramval = getattr(self, param_name)
target_map = get_map_method(num_cpus)
if get_eigenstates:
func = functools.partial(self._esys_for_paramval,
param_name=param_name,
evals_count=evals_count)
with InfoBar(
"Parallel computation of eigenvalues [num_cpus={}]".format(
num_cpus), num_cpus):
eigensystem_mapdata = list(
target_map(
func,
tqdm(param_vals,
desc='Spectral data',
leave=False,
disable=(num_cpus > 1))))
eigenvalue_table, eigenstate_table = recast_esys_mapdata(
eigensystem_mapdata)
else:
func = functools.partial(self._evals_for_paramval,
param_name=param_name,
evals_count=evals_count)
with InfoBar(
"Parallel computation of eigensystems [num_cpus={}]".
format(num_cpus), num_cpus):
eigenvalue_table = list(
target_map(
func,
tqdm(param_vals,
desc='Spectral data',
leave=False,
disable=(num_cpus > 1))))
eigenvalue_table = np.asarray(eigenvalue_table)
eigenstate_table = None
if subtract_ground:
for param_index, _ in enumerate(param_vals):
eigenvalue_table[param_index] -= eigenvalue_table[param_index,
0]
setattr(self, param_name, previous_paramval)
specdata = SpectrumData(eigenvalue_table,
self._get_metadata_dict(),
param_name,
param_vals,
state_table=eigenstate_table)
if filename:
specdata.filewrite(filename)
return SpectrumData(eigenvalue_table,
self._get_metadata_dict(),
param_name,
param_vals,
state_table=eigenstate_table)